---
base_model: haryoaw/scenario-MDBT-TCR-MSV-CL
datasets:
- massive
library_name: transformers
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-NON-KD-PO-COPY-D2_data-AmazonScience_massive_all_1_155
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-NON-KD-PO-COPY-D2_data-AmazonScience_massive_all_1_155

This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR-MSV-CL](https://huggingface.co/haryoaw/scenario-MDBT-TCR-MSV-CL) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5339
- Accuracy: 0.8554
- F1: 0.8295

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 55
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step   | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:------:|:---------------:|:--------:|:------:|
| 0.564         | 0.2672 | 5000   | 0.6868          | 0.8215   | 0.7763 |
| 0.4391        | 0.5344 | 10000  | 0.6652          | 0.8372   | 0.8055 |
| 0.3679        | 0.8017 | 15000  | 0.6546          | 0.8445   | 0.8145 |
| 0.2392        | 1.0689 | 20000  | 0.7360          | 0.8438   | 0.8175 |
| 0.235         | 1.3361 | 25000  | 0.7624          | 0.8490   | 0.8230 |
| 0.2161        | 1.6033 | 30000  | 0.7277          | 0.8492   | 0.8275 |
| 0.2069        | 1.8706 | 35000  | 0.7749          | 0.8471   | 0.8235 |
| 0.145         | 2.1378 | 40000  | 0.8993          | 0.8425   | 0.8102 |
| 0.1482        | 2.4050 | 45000  | 0.8471          | 0.8474   | 0.8196 |
| 0.1479        | 2.6722 | 50000  | 0.8894          | 0.8511   | 0.8280 |
| 0.1379        | 2.9394 | 55000  | 0.8645          | 0.8489   | 0.8240 |
| 0.1063        | 3.2067 | 60000  | 0.9803          | 0.8516   | 0.8284 |
| 0.1077        | 3.4739 | 65000  | 0.9819          | 0.8475   | 0.8260 |
| 0.1036        | 3.7411 | 70000  | 0.9888          | 0.8478   | 0.8242 |
| 0.092         | 4.0083 | 75000  | 1.0324          | 0.8503   | 0.8273 |
| 0.0821        | 4.2756 | 80000  | 1.0974          | 0.8473   | 0.8174 |
| 0.0827        | 4.5428 | 85000  | 1.1001          | 0.8483   | 0.8255 |
| 0.0764        | 4.8100 | 90000  | 1.0927          | 0.8490   | 0.8267 |
| 0.0586        | 5.0772 | 95000  | 1.1587          | 0.8531   | 0.8259 |
| 0.0655        | 5.3444 | 100000 | 1.1937          | 0.8499   | 0.8260 |
| 0.0604        | 5.6117 | 105000 | 1.1844          | 0.8500   | 0.8258 |
| 0.0574        | 5.8789 | 110000 | 1.2190          | 0.8521   | 0.8271 |
| 0.045         | 6.1461 | 115000 | 1.2595          | 0.8533   | 0.8288 |
| 0.0473        | 6.4133 | 120000 | 1.2713          | 0.8527   | 0.8261 |
| 0.0477        | 6.6806 | 125000 | 1.2822          | 0.8527   | 0.8286 |
| 0.0392        | 6.9478 | 130000 | 1.2957          | 0.8562   | 0.8312 |
| 0.0365        | 7.2150 | 135000 | 1.3796          | 0.8526   | 0.8309 |
| 0.0372        | 7.4822 | 140000 | 1.3954          | 0.8526   | 0.8239 |
| 0.0344        | 7.7495 | 145000 | 1.3817          | 0.8542   | 0.8275 |
| 0.0257        | 8.0167 | 150000 | 1.4356          | 0.8554   | 0.8301 |
| 0.0237        | 8.2839 | 155000 | 1.4554          | 0.8546   | 0.8292 |
| 0.0235        | 8.5511 | 160000 | 1.4907          | 0.8553   | 0.8319 |
| 0.0243        | 8.8183 | 165000 | 1.4675          | 0.8540   | 0.8281 |
| 0.0171        | 9.0856 | 170000 | 1.5041          | 0.8552   | 0.8292 |
| 0.0158        | 9.3528 | 175000 | 1.5237          | 0.8558   | 0.8303 |
| 0.0192        | 9.6200 | 180000 | 1.5367          | 0.8548   | 0.8286 |
| 0.0159        | 9.8872 | 185000 | 1.5339          | 0.8554   | 0.8295 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1